Advanced Optimization Methods for The Design of Aluminium Based Battery Enclosures for Electric Vehicles

电动汽车铝基电池外壳设计的高级优化方法

• 批准号: 2891974
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2891974
• 关键词: Advanced; Optimization; Methods; Design; Aluminium

DescriptionClimate change is one of the most important topics in the 21st century. The transportation sector, a major emitter of CO2, must adapt to reduce its impact on the environment. As a part of this sector, the automotive industry is focusing on electric vehicles as one of its strategies to reduce CO2 emissions. As the electric vehicle market grows, manufacturers are developing new vehicle platforms specifically for electric vehicles.One of the challenges is compensating for the added weight of batteries by decreasing vehicle body weight. This is especially important because the additional battery mass reduces the range of an electric vehicle. Thus, lightweight engineering is a crucial part of the vehicle's design, including the battery enclosure's design. Aluminium extrusion profiles for battery enclosures are one of the preferred structures subjected to crash loads, due to their high energy absorption to weight ratio.The design process of such battery enclosures is conducted and expedited using advanced computer-aided engineering tools, cutting down physical tests. Yet, the process still heavily relies on the designer's expertise. Advanced computer-aided engineering tools targeting optimised solutions, based on finer design details like material choice, can accelerate market entry. This, enabling designers to effectively design and optimize battery enclosures, considering material properties and various assembly methods such as welding, bonding, or mechanical fastening. Therefore, enabling lightweight structures that save material and resources. For this purpose, an adopted methodology must be developed.Aims and objectivesThe proposed research aims to develop an optimised design process for electric vehicle battery enclosures. The research is interdisciplinary bridging several disciplines, namely materials science, engineering science, and computer science.Focusing on critical aspects of materials engineering, impact and shock performance of material systems, and optimal design to enhance the topology of aluminium extrusion profile.Applying machine learning techniques for speed improvements of the structure's topology optimisation under consideration of the material properties and impact specific characteristics such as strain rate dependency.The proposed investigation will rely upon stochastic approaches to represent the mechanical performance of materials systems at multiple length scales. The proposed objectives help to enhance and accelerate the design process of battery enclosure structures.Novelty of the research methodologyPhysics-informed neural networks, capable of capturing the underlying mechanics of the problem, are often considered more advanced than purely data-driven methods. By incorporating properties like the material system, rate-dependent material behaviour, and other properties, these networks can notably speed up the topology optimization process while considering the physics. This enables faster optimisation and design of structures subjected to impact loads.Additionally, by conducting a variability analysis, we can gauge the impact of multiple structural properties, including the manufacturing process and rate-dependent material behaviour, on the structure's topology.The proposed methodology aims to expedite and improve the design of battery enclosure profiles while ensuring they meet crash safety requirements.EPSRCThis project falls within the EPSRC engineering design research area.Involved partnerThe EPSRC iCase involves the industrial partner Constellium.

气候变化是21世纪最重要的话题之一。交通运输部门是二氧化碳的主要排放者，必须适应以减少其对环境的影响。作为该行业的一部分，汽车行业正专注于电动汽车，将其作为减少二氧化碳排放的战略之一。随着电动汽车市场的增长，制造商正在开发专门用于电动汽车的新车辆平台。其中一个挑战是通过降低车身重量来补偿电池增加的重量。这一点尤其重要，因为额外的电池质量会减少电动汽车的续航里程。因此，轻量化工程是车辆设计的关键部分，包括电池外壳的设计。铝合金挤压型材是承受碰撞载荷的首选结构之一，因为其具有高的能量吸收重量比。这种电池外壳的设计过程使用先进的计算机辅助工程工具进行并加快，减少了物理测试。然而，这个过程仍然严重依赖于设计师的专业知识。基于材料选择等更精细的设计细节，针对优化解决方案的先进计算机辅助工程工具可以加速市场进入。这使设计人员能够有效地设计和优化电池外壳，考虑材料特性和各种组装方法，如焊接，粘合或机械紧固。因此，能够实现节省材料和资源的轻质结构。为此，必须制定一个采用的方法。Aims and objectivesThe拟议的研究旨在开发一个优化的设计过程，电动汽车电池外壳。该研究是跨学科的桥梁几个学科，即材料科学，工程科学和计算机科学。专注于材料工程的关键方面，材料系统的冲击和冲击性能，应用机器学习技术，在考虑材料特性和冲击特性的前提下，提高了结构拓扑优化的速度所提出的调查将依赖于随机方法来表示在多个长度尺度的材料系统的机械性能。所提出的目标有助于提高和加速电池外壳结构的设计过程。新奇的研究方法物理知情的神经网络，能够捕捉问题的基本机制，往往被认为是比纯粹的数据驱动的方法更先进。通过结合材料系统、速率相关材料行为和其他属性，这些网络可以在考虑物理特性的同时显著加快拓扑优化过程。此外，通过进行变异性分析，我们可以衡量多种结构特性的影响，包括制造工艺和与材料性能相关的速率，该方法旨在加快和改进电池外壳轮廓的设计，同时确保它们满足碰撞安全要求。EPSRC该项目属于福尔斯EPSRC工程设计研究领域。参与合作伙伴EPSRC iCase涉及工业合作伙伴Constellium。